Execution Delay Research Articles

An Optimized Offloaded Task Execution for Smart Cities Applications

Wireless nodes are one of the main components in different applications that are offered in a smart city. These wireless nodes are responsible to execute multiple tasks with different priority levels. As the wireless nodes have limited processing capacity, they offload their tasks to cloud servers if the number of tasks exceeds their task processing capacity. Executing these tasks from remotely placed cloud servers causes a significant delay which is not required in sensitive task applications. This execution delay is reduced by placing fog computing nodes near these application nodes. A fog node has limited processing capacity and is sometimes unable to execute all the requested tasks. In this work, an optimal task offloading scheme that comprises two algorithms is proposed for the fog nodes to optimally execute the time-sensitive offloaded tasks. The first algorithm describes the task processing criteria for local computation of tasks at the fog nodes and remote computation at the cloud server. The second algorithm allows fog nodes to optimally scrutinize the most sensitive tasks within their task capacity. The results show that the proposed task execution scheme significantly reduces the execution time and most of the time-sensitive tasks are executed.

Multi-User Dynamic Computation Offloading and Resource Allocation in 5G MEC Heterogeneous Networks with Static and Dynamic Subchannels

With the rapid development of Mobile Edge Computing (MEC) technology, the computationally intensive requests of end devices can be offloaded to MEC servers directly, which equipped at the edge of wireless networks. Through offloading, the performances such as the execution delay as well as the energy consumption can be effectively improved, which can significantly enhance the quality of user experience. Given the dynamics and randomness of computation requests arrival, the energy in the battery, the radio network environment, and the computation resource in the MEC server, it is a challenge to perform efficient offloading. Based on these problems, this paper proposes dynamic optimization schemes with queuing theory for the cases of the static subchannel and dynamic subchannel during a time slot separately in 5G MEC heterogeneous networks with multiple MDs equipped with the function of energy harvesting. In the schemes, offloading decisions and radio allocation strategies will be dynamically coordinated. They are also jointly allocated along with changing wireless communication resources and computation demands aiming to minimize the system average execution delay. Specifically, it is assumed that the offloading requests can be transmitted through either macro base stations or small base stations. In the case of the static subchannel, a joint resource allocation and computation offloading scheme based on Lyapunov optimization and Simulated Annealing Genetic Algorithm (SAGA) is put forward. As for the dynamic subchannel, the master-and-slave model is adopted and solved by SAGA and Sequential Quadratic Programming (SQP) method. At last, the effectiveness of the proposed schemes is verified through several simulations.

A Deep Reinforcement Learning-Based Offloading Scheme for Multi-Access Edge Computing-Supported eXtended Reality Systems

In recent years, eXtended Reality (XR) applications have been widely employed in various scenarios, e.g., health care, education, manufacturing, etc. Such application are now easily accessible via mobile phones, tablets, or wearable devices. However, such devices normally suffer from constraints in terms of battery capacity and processing power, limiting the range of applications supported or lowering Quality of Experience. One effective way to address these issues is to offload the computation tasks to the edge servers that are deployed at the network edges, e.g., base stations or WiFi access point, etc. This communication fashion, also named as Multi-access Edge Computing (MEC), is proposed to overcome the limitations in terms of long latency due to long propagation distance of traditional cloud computing approach. XR devices, that are limited in computation resources and energy, can then benefit from offloading the computation intensive tasks to MEC servers. However, as XR applications are comprised of multiple tasks with variety of requirements in terms of latency and energy consumption, it is important to make decision whether one task should be offloaded to MEC server or not. This paper proposes a Deep Reinforcement Learning-based offloading scheme for XR devices (DRLXR). The proposed scheme is used to train and derive the close-to optimal offloading decision whereas optimizing a utility function optimization equation that considers both energy consumption and execution delay at XR devices. The simulation results show how our proposed scheme outperforms the other counterparts in terms of total execution latency and energy consumption.

Deep Reinforcement Learning-Based Task Offloading for Parked Vehicle Cooperation in Vehicular Edge Computing

Vehicular edge computing (VEC) has greatly enhanced the quality of vehicle service with low latency and high reliability. However, in some areas not covered by roadside infrastructures or in cases when the infrastructures are damaged or fail, the offloaded tasks cannot have the chance to be performed. Even in the areas deployed with infrastructures, when a large number of offloaded tasks are generated, the edge servers may not be capable of processing them in time, owing to their computing resources constraint. Based on the above observations, we proposed the idea of parked vehicle cooperation in VEC, which uses roadside parked vehicles with underutilized computational resources to cooperate with each other to perform the compute-intensive tasks. Our approach aims to overcome the challenge brought by infrastructure lacking or failure and make up for the shortage of computing resources in VEC. In our approach, firstly, the roadside parked vehicles are managed as different parking clusters. Then, the optimal amount of resources required for each offloaded task is analyzed. Furthermore, a task offloading algorithm based on deep reinforcement learning (DRL) is proposed to minimize the total cost, which is composed of the task execution delay and the energy consumption overhead of the parked vehicles for executing the task. A large number of simulation results show that, compared with other algorithms, our approach not only has the highest task completion execution successful rate, but also has the lowest task execution cost.

Split Computing and Early Exiting for Deep Learning Applications: Survey and Research Challenges

Mobile devices such as smartphones and autonomous vehicles increasingly rely on deep neural networks (DNNs) to execute complex inference tasks such as image classification and speech recognition, among others. However, continuously executing the entire DNN on mobile devices can quickly deplete their battery. Although task offloading to cloud/edge servers may decrease the mobile device’s computational burden, erratic patterns in channel quality, network, and edge server load can lead to a significant delay in task execution. Recently, approaches based on split computing (SC) have been proposed, where the DNN is split into a head and a tail model, executed respectively on the mobile device and on the edge server. Ultimately, this may reduce bandwidth usage as well as energy consumption. Another approach, called early exiting (EE), trains models to embed multiple “exits” earlier in the architecture, each providing increasingly higher target accuracy. Therefore, the tradeoff between accuracy and delay can be tuned according to the current conditions or application demands. In this article, we provide a comprehensive survey of the state of the art in SC and EE strategies by presenting a comparison of the most relevant approaches. We conclude the article by providing a set of compelling research challenges.

Joint optimization of task caching, computation offloading and resource allocation for mobile edge computing

Applications with sensitive delay and sizeable data volumes, such as interactive gaming and augmented reality, have become popular in recent years. These applications pose a huge challenge for mobile users with limited resources. Computation offloading is a mainstream technique to reduce execution delay and save energy for mobile users. However, computation offloading requires communication between mobile users and mobile edge computing (MEC) servers. Such a mechanism would difficultly meet users' demand in some data-hungry and computation-intensive applications because the energy consumption and delay caused by transmissions are considerable expenses for users. Caching task data can effectively reduce the data transmissions when users offload their tasks to the MEC server. The limited caching space at the MEC server calls for judiciously decide which tasks should be cached. Motivated by this, we consider the joint optimization of computation offloading and task caching in a cellular network. In particular, it allows users to proactively cache or offload their tasks at the MEC server. The objective of this paper is to minimize the system cost, which is defined as the weighted sum of task execution delay and energy consumption for all users. Aiming at establishing optimal performance bound for the system design, we formulate an optimization problem by jointly optimizing the task caching, computation offloading, and resource allocation. The problem is a challenging mixed-integer non-linear programming problem and is NP-hard in general. To solve it efficiently, by using convex optimization, Karmarkar's algorithm and the proposed fast search algorithm, we obtain an optimal solution of the formulated problem with manageable computational complexity. Extensive simulation results show that in comparison to some representative benchmark methods, the proposed solution can effectively reduce the system cost.

Task Scheduling Strategy for Industrial Control Network based on Cloud-Edge-End Fusion Framework

Industrial tasks with traditional cloud architecture have many disadvantages, which are mainly longer delay of data transmission and difficulty in software deployment. In view of the above disadvantages, this paper used containers to solve the problems for industrial software deployment and upgrade. This paper uses a cloud-edge-end framework to deploy tasks with high delay on the edge of the network or even on the device side, so as to reduce the delay of data transmission and computing pressure. At the same time, the task scheduling strategy based on the particle swarm optimization algorithm is used to search for the node with the minimum task delay among multiple nodes to realize the task allocation optimization. Finally, through the comparison experiment, it is concluded that the task scheduling strategy for industrial control networks based on a cloud-edge-end fusion framework can better reduce the task execution delay and reduce the difficulty of software deployment.

Secure Task Offloading in Blockchain-Enabled Mobile Edge Computing With Deep Reinforcement Learning

Mobile Edge Computing (MEC) is a promising and fast-developing paradigm that provides cloud services at the edge of the network. MEC enables IoT devices to offload and execute their real-time applications at the proximity of these devices with low latency. Such applications include efficient manufacture inspection, virtual/augmented reality, image recognition, Internet of Vehicles (IoV), and e-Health. However, task offloading experiences security and privacy attacks such as data tampering, private data leakage, data replication, etc. To this end, in this paper, we propose a new blockchain-based framework for secure task offloading in MEC systems with guaranteed performance in terms of execution delay and energy consumption. First, blockchain technology is introduced as a platform to achieve data confidentiality, integrity, authentication, and privacy of task offloading in MEC. Second, we formulate an integration model of resource allocation and task offloading for a multi-user with multi-task MEC systems to optimize the energy and time cost. This is an NP-hard problem because of the curse-of-dimensionality and dynamic characteristics challenges of the considered scenario. Therefore, a deep reinforcement learning-based algorithm is developed to derive the close-optimal task offloading decision efficiently. Theoretical analysis and experimental results demonstrate that the proposed framework is resilient to several task offloading security attacks and it can save about 22.2% and 19.4% of system consumption with respect to the local and edge execution scenarios. Moreover, the benchmark analysis proves that the framework consumes few resources in terms of memory and disk usage, CPU utilization, and transaction throughput.

Resource scheduling in mobile edge computing using improved ant colony algorithm for space information network

SummaryWith the development of space information network (SIN), new network applications are emerging. Satellites are not only used for storage and transmission but also gradually used for calculation and analysis, so the demand for resources is increasing. But satellite resources are still limited. Mobile edge computing (MEC) is considered an effective technique to reduce the pressure on satellite resources. To solve the problem of task execution delay caused by limited satellite resources, we designed Space Mobile Edge Computing Network (SMECN) architecture. According to this architecture, we propose a resource scheduling method. First, we decompose the user tasks in SMECN, so that the tasks can be assigned to different servers. An improved ant colony resource scheduling algorithm for SMECN is proposed. The heuristic factors and pheromones of the ant colony algorithm are improved through time and resource constraints, and the roulette algorithm is applied to route selection to avoid falling into the local optimum. We propose a dynamic scheduling algorithm to improve the contract network protocol to cope with the dynamic changes of the SIN and dynamically adjust the task execution to improve the service capability of the SIN. The simulation results show that when the number of tasks reaches 200, the algorithm proposed in this paper takes 17.52% less execution time than the Min‐Min algorithm, uses 9.58% less resources than the PSO algorithm, and achieves a resource allocation rate of 91.65%. Finally, introducing dynamic scheduling algorithms can effectively reduce task execution time and improve task availability.

Federated-Reinforcement-Learning-Enabled Joint Communication, Sensing, and Computing Resources Allocation in Connected Automated Vehicles Networks

For future connected automated vehicles (CAVs) networks, the joint optimization of communication, sensing, and computing resources is crucial to guarantee the performance of cooperative automated driving’s safety, which is attracting more and more attention. However, the existing works have not considered the low-latency requirement for the raw perception data sharing with both wireless communication link capability and computing efficiency constraints, causing a serious threat to the cooperative automated driving’s safety in CAVs networks. In this article, a vehicle–road–base station cooperation architecture is designed, and a federated reinforcement learning (FRL)-based task offloading and resource allocation algorithm in the CAVs network is proposed to reduce the task execution delay with different communication and computing constraints. The problem of execution delay minimization is theoretically formulated and analyzed under three task practical offloading modes. To adapt to the dynamic topology of the CAVs network, we design a deep reinforcement learning algorithm to achieve the optimal task offloading and resource allocation. To further reduce the data transmission overhead of the centralized reinforcement learning algorithm, the FRL-enabled algorithm is proposed to minimize the execution delay of the optimal task offloading and resource allocation among multiple CAVs. Both the simulation and hardware testbed results verify that the proposed algorithms can not only reduce the execution delay and the communication overhead but also improve the system throughput.

Cloud–edge collaboration-based bi-level optimal scheduling for intelligent healthcare systems

The development of intelligent healthcare systems (IHS) has raised the added value of digital medical data. With an efficient exploitation of digital medical data for diagnosis assistance, federated learning (FL) is promising in future digital health care. However, in multiple task performances, federated nodes deployed at the edge of IHS are constrained by computing and storage resources, as well as increased privacy breach risks. On account of these challenges, this paper proposes a more elaborated cloud–edge collaboration (CEC) framework of IHS combining FL and blockchain. Thus, a bi-level optimization scheduling IHS model is proposed, considering the large-scale access requirement of distributed generation (DG), energy storage (ES) and controllable load (CL) access to the IHS. Simulation results confirm an effective reduction of execution delay and power consumption, and a better interest coordination among multi-stakeholders.

Mercury: Instruction Pipeline Aware Code Generation for Simulink Models

Simulink is a widely used model-driven design environment for supporting the simulation and code generation of embedded applications. To improve the quality of the code generated from Simulink models, state-of-the-art code generators employ various high-level optimizations, like eliminating local variables. However, they overlook the compatibility between code and the low-level processor architecture, especially the instruction pipeline. Consequently, instruction pipeline stalls occur frequently, leading to additional delays in instruction execution, as well as limited efficiency for deployed the embedded software. In this article, we propose Mercury, an instruction pipeline aware code generator for Simulink models which utilizes data dependencies between actors to decrease the instruction pipeline stalls of the generated code. First, Mercury collects data dependencies through model dataflow traversal and records the property of each actor. Then, Mercury approximately estimates the execution latency of required instructions fetched from corresponding actors and uses a topology-based method to obtain candidate actors for code synthesis. Finally, Mercury adopts the least penalty priority to iteratively select the most suitable actor for code synthesis and releases data dependencies with its subsequent actors. We implemented and evaluated Mercury on benchmark Simulink models (Su et al., 2021) as well as a real industrial model. Compared to the official tool Simulink Embedded Coder and the state-of-the-art academic tool DFSynth, Mercury outperformed them by 9.7%–33.4% and 9.2%–59.4% in terms of the execution time of the generated code across different architectures, respectively. The statistics also demonstrate that the generated code of Mercury increases utilization of pipeline slots by 11.0%–37.1% and 10.6%–50.0%, respectively.

Computation Offloading for Partitionable Applications in Dense Networks: An Evolutionary Game Approach

Mobile-edge computing (MEC) is a burgeoning paradigm that performs computation close to the network edge. In MEC, computation offloading is an essential mechanism to alleviate the limitations of user equipment (UEs) by offloading computation-intensive and latency-sensitive tasks to edge servers deployed in base stations (BSs). For the partitionable task model, multi-BS offloading makes the parallel execution of tasks possible, which can further reduce the execution delay. In this article, we consider a MEC architecture in a dense network with multiple BSs and multiple UEs and formulate a computation offloading problem for partitionable tasks with the goal of minimizing the task completion time. We jointly optimize the computation and radio resources considering the heterogeneity of UEs’ geographic locations and task types. Specifically, we construct a task offloading game using the evolutionary game theory (EGT). We apply replicator dynamics to model the offloading decision process and obtain the evolutionary equilibrium, which is proved to be an evolutionary stable strategy. Based on the theoretical analysis, we propose an EGT-based offloading mechanism to achieve a suboptimal solution in the case of limited numbers of subtasks. The extensive evaluation results demonstrate the effectiveness of our proposed mechanism compared with the centralized optimal solution.

Differential Pricing-Based Task Offloading for Delay-Sensitive IoT Applications in Mobile Edge Computing System

With the evolutionary development of the Internet of Things (IoT), the demand for delay-sensitive applications has increased. In this manner, mobile-edge computing (MEC) has emerged as a promising technology to run these applications on mobile devices. Several studies have been conducted on pricing schemes in the MEC environment, and they have focused on the amount of offloaded data in pricing. However, under the previously proposed pricing policy, the server resource usage of users is not properly reflected in the payment, so users only try to occupy the server resources as much as possible without any consideration. To solve this problem, this article newly proposes a differential pricing scheme in which the unit price per second is determined based on the user’s usage of server computational resources. Also, it suggests the user’s optimal offloading strategy and server’s equilibrium pricing strategy by formulating Stackelberg game based on the proposed pricing scheme and execution delay. Several numerical results show that the proposed pricing scheme and strategies can solve the problems and can increase the efficiency of edge server’s computational resources.

Research on Multi-Terminal’s AC Offloading Scheme and Multi-Server’s AC Selection Scheme in IoT

Mobile Edge Computing (MEC) technology and Simultaneous Wireless Information and Power Transfer (SWIPT) technology are important ones to improve the computing rate and the sustainability of devices in the Internet of things (IoT). However, the system models of most relevant papers only considered multi-terminal, excluding multi-server. Therefore, this paper aims at the scenario of IoT with multi-terminal, multi-server and multi-relay, in which can optimize the computing rate and computing cost by using deep reinforcement learning (DRL) algorithm. Firstly, the formulas of computing rate and computing cost in proposed scenario are derived. Secondly, by introducing the modified Actor-Critic (AC) algorithm and convex optimization algorithm, we get the offloading scheme and time allocation that maximize the computing rate. Finally, the selection scheme of minimizing the computing cost is obtained by AC algorithm. The simulation results verify the theoretical analysis. The algorithm proposed in this paper not only achieves a near-optimal computing rate and computing cost while significantly reducing the program execution delay, but also makes full use of the energy collected by the SWIPT technology to improve energy utilization.

Implicit Extended Kalman Filter for Optical Terrain Relative Navigation Using Delayed Measurements

The exploration of celestial bodies such as the Moon, Mars, or even smaller ones such as comets and asteroids, is the next frontier of space exploration. One of the most interesting and attractive purposes from the scientific point of view in this field, is the capability for a spacecraft to land on such bodies. Monocular cameras are widely adopted to perform this task due to their low cost and system complexity. Nevertheless, image-based algorithms for motion estimation range across different scales of complexities and computational loads. In this paper, a method to perform relative (or local) terrain navigation using frame-to-frame features correspondences and altimeter measurements is presented. The proposed image-based approach relies on the implementation of the implicit extended Kalman filter, which works using nonlinear dynamic models and corrections from measurements that are implicit functions of the state variables. In particular, here, the epipolar constraint, which is a geometric relationship between the feature point position vectors and the camera translation vector, is employed as the implicit measurement fused with altimeter updates. In realistic applications, the image processing routines require a certain amount of time to be executed. For this reason, the presented navigation system entails a fast cycle using altimeter measurements and a slow cycle with image-based updates. Moreover, the intrinsic delay of the feature matching execution is taken into account using a modified extrapolation method.

Multi-Agent Deep Reinforcement Learning for Task Offloading in UAV-Assisted Mobile Edge Computing

Mobile edge computing can effectively reduce service latency and improve service quality by offloading computation-intensive tasks to the edges of wireless networks. Due to the characteristic of flexible deployment, wide coverage and reliable wireless communication, unmanned aerial vehicles (UAVs) have been employed as assisted edge clouds (ECs) for large-scale sparely-distributed user equipment. Considering the limited computation and energy capacities of UAVs, a collaborative mobile edge computing system with multiple UAVs and multiple ECs is investigated in this paper. The task offloading issue is addressed to minimize the sum of execution delays and energy consumptions by jointly designing the trajectories, computation task allocation, and communication resource management of UAVs. Moreover, to solve the above non-convex optimization problem, a Markov decision process is formulated for the multi-UAV assisted mobile edge computing system. To obtain the joint strategy of trajectory design, task allocation, and power management, a cooperative multi-agent deep reinforcement learning framework is investigated. Considering the high-dimensional continuous action space, the twin delayed deep deterministic policy gradient algorithm is exploited. The evaluation results demonstrate that our multi-UAV multi-EC task offloading method can achieve better performance compared with the other optimization approaches.

Solving User Priority in Cloud Computing Using Enhanced Optimization Algorithm in Workflow Scheduling.

Cloud computing is a procedure of stockpiling as well as retrieval of data or computer services over the Internet that allows all its users to remotely access the data centers. Cloud computing provides all required services to the users, but every platform has its share of pros and cons, and another major problem in the cloud is task scheduling or workflow scheduling. Multiple factors are becoming a challenge for scheduling in cloud computing namely the heterogeneity of resources, tasks, and user priority. User priority has been encountered as the most challenging problem during the last decade as the number of users is increasing worldwide. This issue has been resolved by an advanced encryption standard (AES) algorithm, which decreases the response time and execution delay of the user-request. There are multifarious tasks, for instance, deploying the data on the cloud, that will be executed according to first come first serve (FCFS) and not on the payment basis, which provides an ease to the users. These investigated techniques are 30.21%, 25.20%, 25.30%, 30.25%, 24.26%, and 36.9 8% improved in comparison with the traditional FFOA, DE, ABC, PSO, GA, and ETC, respectively. Moreover, during iteration number 5, this approach is 15.20%, 20.22%, 30.56%, 26.30%, and 36.23% improved than that of the traditional techniques FFOA, DE, ABC, PSO, GA, and ETC, respectively. This investigated method is more efficient and applicable in certain arenas where user priority is the primary concern and can offer all the required services to the users without any interruption.

Users' experience matter: Delay sensitivity-aware computation offloading in mobile edge computing

As a promising computing paradigm, Mobile Edge Computing (MEC) provides communication and computing capability at the edge of the network to address the concerns of massive computation requirements, constrained battery capacity and limited bandwidth of the Internet of Things (IoT) systems. Most existing works on mobile edge task ignores the delay sensitivities, which may lead to the degraded utility of computation offloading and dissatisfied users. In this paper, we study the delay sensitivity-aware computation offloading by jointly considering both user's tolerance towards delay of task execution and the network status under computation and communication constraints. Specifically, we use a specific multi-user and multi-server MEC system to define the latency sensitivity of task offloading based on the analysis of delay distribution of task categories. Then, we propose a scoring mechanism to evaluate the sensitivity-dependent utility of task execution and devise a Centralized Iterative Redirection Offloading (CIRO) algorithm to collect all information in the MEC system. By starting with an initial offloading strategy, the CIRO algorithm enables IoT devices to cooperate and iteratively redirect task offloading decisions to optimize the offloading strategy until it converges. Extensive simulation results show that our method can significantly improve the utility of computation offloading in MEC systems and has lower time complexity than existing algorithms.

Balanced multi-access edge computing offloading strategy in the Internet of things scenario

Internet of things devices can offload part of the tasks to the edge servers through the wireless network, thus the computing pressure and energy consumption are reduced. However, this will increase the cost of communication. When designing the offloading strategy for the edge computing scenario of the Internet of things, it is necessary to maintain the balance between task execution energy and experiment. Therefore, this paper proposes an offloading strategy which can optimize the energy consumption and time delay of task execution at the same time. This strategy satisfies different preferences of users. First, the above task is modeled as a multi-objective optimization problem, and the Pareto solution set is found by improving the strength Pareto evolutionary algorithm (SPEA2). Based on the Pareto set, the offloading strategy satisfying the requires of users with different preferences by offloading cost estimation. Second, a simulation experiment is carried out to verify the robustness of the improved SPEA2 algorithm under the influence of different main parameters. Compared with other representative algorithms, the improved SPEA2 algorithm is proved to minimize the task execution delay and energy consumption jointly.